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An intelligent neural network algorithm for uncertainty handling in sensor failure scenario of food quality assurance model

Deepa S. N., Jayalakshmi N. Yogamba

Computer Assisted Methods in Engineering and Science

2022

Vol. 29, no. 1-2 spec.

105--123

The quality of food is usually tested by sensing the product odor using e-nose technique.However, in a real-time testing environment, some of the employed sensors may fail tooperate, which imposes great uncertainty on the food quality assurance model. To handlethe uncertainty, a support vector machine (SVM) classifier algorithm is developed todeal with the failure sensor effect using a data imputation strategy. The proposed modelis evaluated experimentally by means of benchmark datasets, and validated in a real-time environment by programming an Arduino-UNO controller in the internet of things(IoT) environment.